---
title: "COVID-19"
author: "Ekarin Eric Pongpipat, M.A."
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    theme: lumen
    vertical_layout: scroll
---

```{r setup, include=FALSE}
#runtime: shiny
knitr::opts_chunk$set(out.width = '100%', out.height = '100%')
packages <- c("tidyverse", "covid19us", "tidyselect", "plotly", "TTR", "glue")
xfun::pkg_attach2(packages, message = F)
options(scipen=999)
```

```{r data}
base_dir <- "~/Desktop/func_test/covid19"
df <- read.csv(glue("{base_dir}/data/data.csv")) %>%
  as_tibble() %>%
  mutate(date = as.Date(date))
```

```{r viz}
viz <- function(data, regions = 'all', metrics,
                smooth_value = TRUE,
                proportion_value = TRUE) {
  
  if (regions == 'all') {
    df_fig <- data
  } else {
    df_fig <- data %>%
      filter(region %in% regions)
  }
  
  df_fig <- df_fig %>%
    filter(
      metric %in% metrics,
      smooth == smooth_value,
      proportion == proportion_value
    )
  
  #print(region_end)
  
  fig <- ggplot(df_fig, aes(date, value, color = region)) +
    geom_line() +
    labs(x = "\nDate",
         color = "Region") + 
    theme_minimal() #+
    #scale_y_continuous(sec.axis = sec_axis(~ ., breaks = region_end))

  
  if (proportion_value == FALSE) {
    fig <- fig +
      labs(y = "No. of People\n")
  } else {
    fig <- fig +
      labs(y = "% of Respective Population\n") +
      scale_y_continuous(labels = scales::percent)
  }
  return(fig)
  
}
```

Summary
====

Row
----

### Daily Hospitalizations (Frequency)

```{r}
viz(df, 'all', "daily_total_hospitalizations", T, F)
```

### Daily Hospitalizations (Proportion)

```{r}
viz(df, 'all', "daily_total_hospitalizations", T, T)
```

Row
----

### Daily Deaths (Frequency)

```{r}
viz(df, 'all', "daily_deaths", T, F)
```

### Daily Deaths (Proportion)

```{r}
viz(df, 'all', "daily_deaths", T, T)
```

U.S.
====

Row
----

### Daily Hospitalizations (Frequency)

```{r}
viz(df, c('CA', 'TX', 'LA County', 'Dallas County'), "daily_total_hospitalizations", T, F)
```

### Daily Hospitalizations (Proportion)

```{r}
viz(df, c('CA', 'TX', 'LA County', 'Dallas County'), "daily_total_hospitalizations", T, T)
```

Row
----

### Daily Deaths (Frequency)

```{r}
viz(df, c('CA', 'TX', 'LA County', 'Dallas County'), "daily_deaths", T, F)
```

### Daily Deaths (Proportion)

```{r}
viz(df, c('CA', 'TX', 'LA County', 'Dallas County'), "daily_deaths", T, T)
```

Thailand 
====

Row
----

### Daily Hospitalizations (Frequency)

```{r}
viz(df, c('Thailand'), "daily_total_hospitalizations", T, F)
```

### Daily Hospitalizations (Proportion)

```{r}
viz(df, c('Thailand'), "daily_total_hospitalizations", T, T)
```

Row
----

### Daily Deaths (Frequency)

```{r}
viz(df, c('Thailand'), "daily_deaths", T, F)
```

### Daily Deaths (Proportion)

```{r}
viz(df, c('Thailand'), "daily_deaths", T, T)
```

Guatemala
====

Row
----

### Daily Hospitalizations (Frequency)

```{r}
viz(df, c('Guatemala'), "daily_total_hospitalizations", T, F)
```

### Daily Hospitalizations (Proportion)

```{r}
viz(df, c('Guatemala'), "daily_total_hospitalizations", T, T)
```

Row
----

### Daily Deaths (Frequency)

```{r}
viz(df, c('Guatemala'), "daily_deaths", T, F)
```

### Daily Deaths (Proportion)

```{r}
viz(df, c('Guatemala'), "daily_deaths", T, T)
```